Slide3:

In moving further into the age of machine intelligence and automated reasoning, we have reached a point where we can speak, without exaggeration, of systems which have a high machine IQ (MIQ). The Web and especially search engines—with Google at the top—fall into this category. In the context of the Web, MIQ becomes Web IQ, or WIQ, for short. PREAMBLE

WEB INTELLIGENCE:

WEB INTELLIGENCE Existing search engines have many remarkable capabilities. However, what is not among them is the deduction capability—the capability to synthesize an answer to a query by drawing on bodies of information which reside in various parts of the knowledge base.

CONTINUED:

CONTINUED A question-answering system is by definition a system which has deduction capability. One of the principal goals of Web intelligence is that of evolving search engines into question-answering systems. Achievement of this goal requires a quantum jump in the WIQ of existing search engines.

QUANTUM JUMP IN WIQ:

QUANTUM JUMP IN WIQ Can a quantum jump in WIQ be achieved through the use of existing tools such as the Semantic Web and ontology-centered systems--tools which are based on bivalent logic and bivalent-logic-based probability theory?

CONTINUED:

CONTINUED It is beyond question that, in recent years, very impressive progress has been made through the use of such tools. But, a view which is advanced in the following is that bivalent-logic- based methods have intrinsically limited capability to address complex problems which arise in deduction from information which is pervasively ill-structured, uncertain and imprecise.

WORLD KNOWLEDGE:

WORLD KNOWLEDGE The major problem is World Knowledge

WHAT IS WORLD KNOWLEDGE?:

WHAT IS WORLD KNOWLEDGE? world knowledge is acquired through experience and education
examples
usually it is hard to find parking near the campus between 9 and 5
big cars are safer than small cars
few professors are rich

WORLD KNOWLEDGE AND THE WEBKEY POINTS:

WORLD KNOWLEDGE AND THE WEB KEY POINTS world knowledge plays a pivotal role in human cognition
in particular, world knowledge forms the basis for disambiguation, decision-making, deduction and search
specific: Helsinki is the capital of Finland
general: Icy roads are slippery

WORLD KNOWLEDGE: EXAMPLES:

WORLD KNOWLEDGE: EXAMPLES specific:
if Robert works in Berkeley then it is likely that Robert lives in or near Berkeley
if Robert lives in Berkeley then it is likely that Robert works in or near Berkeley
generalized:
if A/Person works in B/City then it is likely that A lives in or near B
precisiated:
Distance (Location (Residence (A/Person), Location (Work (A/Person) isu near
protoform: F (A (B (C)), A (D (C))) isu R

CONTINUED:

the web is, in the main, a repository of specific world knowledge
Semantic Web and related systems serve to enhance the performance of search engines by adding to the web a collection of relevant fragments of world knowledge
the problem is that much of world knowledge, and especially general world knowledge, consists of perceptions
CONTINUED

CONTINUED:

perceptions are intrinsically imprecise
imprecision of perceptions stands in the way of representing the meaning of perceptions through the use of conventional bivalent-logic-based languages
to deal with perceptions and world knowledge, new tools are needed
of particular relevance to enhancement of web intelligence are Precisiated Natural Language (PNL) and Protoform Theory (PFT) CONTINUED

LIMITATIONS OF SEARCH ENGINESTEST QUERIES:

LIMITATIONS OF SEARCH ENGINES TEST QUERIES How many Ph.D.’s in computer science were produced by European universities in 1996?
Age of the President of Finland?
Name of the King of Finland?
Largest port in Switzerland?
Smallest port in Canada?
Number of lakes in Finland?

TEST QUERY (GOOGLE):

TEST QUERY (GOOGLE) distance between largest city in Spain and largest city in Portugal: failure
largest city in Spain: Madrid (success)
largest city in Portugal: Lisbon (success)
distance between Madrid and Lisbon (success)

TEST QUERY (GOOGLE):

TEST QUERY (GOOGLE) population of largest city in Spain: failure
largest city in Spain: Madrid, success
population of Madrid: success

CONTINUED:

CONTINUED AZ of Tourism - Holiday and Vacation guide. Offers comprehensive and continuously updated information on tourism, accommodation and entertainment for many major world cities and allows people to book ... www.a-zoftourism.com/atoz-of-cities-in-UK.htm - 41k - Cached - Similar pages
SAIF - Sveriges Akademiska Idrottsförbund ... Austria Finland Georgien Hungary Netherlands (Holland) Japan Sweden Switzerland Preliminary schedule ... role in merchandising, and hosts the largest port in Sweden ... www.studentidrott.nu/floorball/index.asp - 22k - Sep 2, 2003 - Cached - Similar pages
Hotels Rotterdam. Tourism Rotterdam. Accommodation Rotterdam. ... ... largest city of the Netherlands and the world's largest port. ... The port has several natural advantages, the most ... from as far away as Switzerland, France and ... www.hotels-holland.com/info/Rotterdam/rotterdam.htm - 9k - Cached - Similar pages

RELEVANCE:

RELEVANCE a concept which has a position of centrality in search is that of relevance
and yet, there is no definition of relevance
relevance is a matter of degree
relevance cannot be defined within the conceptual structure of bivalent logic
to define relevance, what is needed is PNL (Precisiated Natural Language)

EXAMPLE: DECISION PROBLEM:

EXAMPLE: DECISION PROBLEM should I insure my car against theft?
decision-relevant information: probability that my car may be stolen?
query: ?q: what is the probability that my car may be stolen?
query-relevant information: information about my car and me; information in police department and insurance company files
the answer yielded by bivalent-logic-based probability theory is: the probability that my car may be stolen is between 0 and 1

RELEVANCE FUNCTION:

RELEVANCE FUNCTION Rel (q/p)= degree to which p constrains the meaning
of q, with p and q expressed as generalized
constraints compositionality:
can Rel (q/p^r) be expressed as a function of
Rel (q/p) and Rel (q/r)?

Slide28:

PROTOFORM LANGUAGE

THE CONCEPT OF A PROTOFORM:

THE CONCEPT OF A PROTOFORM CWP PNL PFL Protoform Language

WHAT IS A PROTOFORM?:

WHAT IS A PROTOFORM? protoform = abbreviation of prototypical form
informally, a protoform, A, of an object, B, written as A=PF(B), is an abstracted summary of B
usually, B is lexical entity such as proposition, question, command, scenario, decision problem, etc
more generally, B may be a relation, system, geometrical form or an object of arbitrary complexity
usually, A is a symbolic expression, but, like B, it may be a complex object
the primary function of PF(B) is to place in evidence the deep semantic structure of B

THE CONCEPT OF PROTOFORM AND RELATED CONCEPTS:

TRANSLATION FROM NL TO PFL:

TRANSLATION FROM NL TO PFL examples
Most Swedes are tall Count (A/B) is Q
Eva is much younger than Pat (A (B), A (C)) is R
usually Robert returns from work at about 6pm
Prob {A is B} is C Eva much younger Age Pat Age usually about 6 pm Time (Robert returns from work)

MULTILEVEL STRUCTURES:

MULTILEVEL STRUCTURES An object has a multiplicity of protoforms
Protoforms have a multilevel structure
There are three principal multilevel structures
Level of abstraction ()
Level of summarization ()
Level of detail ()
For simplicity, levels are implicit
A terminal protoform has maximum level of abstraction
A multilevel structure may be represented as a lattice

ABSTRACTION LATTICE:

ABSTRACTION LATTICE example most Swedes are tall Q Swedes are tall most A’s are tall most Swedes are B Q Swedes are B Q A’s are tall most A’s are B’s Q Swedes are B Q A’s are B’s Count(B/A) is Q

LEVELS OF SUMMARIZATION:

LEVELS OF SUMMARIZATION example
p: it is very unlikely that there will be a significant increase in the price of oil in the near future
PF(p): Prob(E) is A very.unlikely significant increase in the price of oil in the near future

CONTINUED:

Slide40:

largest port in Canada?
second tallest building in San Francisco
PROTOFORM OF A QUERY X A B ?X is selector (attribute (A/B)) San Francisco buildings height 2nd tallest

PROTOFORMAL SEARCH RULES:

PROTOFORMAL SEARCH RULES example
query: What is the distance between the largest city in Spain and the largest city in Portugal?
protoform of query: ?Attr (Desc(A), Desc(B))
procedure
query: ?Name (A)|Desc (A)
query: Name (B)|Desc (B)
query: ?Attr (Name (A), Name (B))

ORGANIZATION OF WORLD KNOWLEDGE EPISTEMIC (KNOWLEDGE-DIRECTED) LEXICON (EL) (ONTOLOGY-RELATED) i (lexine): object, construct, concept (e.g., car, Ph.D. degree)
K(i): world knowledge about i (mostly perception-based)
K(i) is organized into n(i) relations Rii, …, Rin
entries in Rij are bimodal-distribution-valued attributes of i
values of attributes are, in general, granular and context-dependent network of nodes and links wij= granular strength of association between i and j i j rij K(i) lexine wij

EPISTEMIC LEXICON:

EPISTEMIC LEXICON lexinei lexinej rij: i is an instance of j (is or isu)
i is a subset of j (is or isu)
i is a superset of j (is or isu)
j is an attribute of i
i causes j (or usually)
i and j are related rij

THE CONCEPT OF i-PROTOFORM:

THE CONCEPT OF i-PROTOFORM i-protoform: idealized protoform
the key idea is to equate the grade of membership, µA(u), of an object, u, in a fuzzy set, A, to the distance of u from an i-protoform
this idea is inspired by E. Rosch’s work (ca 1972) on the theory of prototypes i-protoform distance of u
from i-protoform A object d U d is defined via PNL fuzzy set u

i-PROTOFORM-BASED DEFINITION OF EXPECTED VALUE:

CONTINUED:

CONTINUED U gn 0 normalized probability
density of X i-protoform = E(X) E(X) is a fuzzy set
grade of membership of a particular function, E*(X), in the fuzzy set of expected value of X is the distance of E*(X) form best-fitting i-protoform

I. PROTOFORMS OF GEOMETRICAL FORMS:

I. PROTOFORMS OF GEOMETRICAL FORMS line
square
circle
ellipse
i.protoform of an oval object is an ellipsoid
degree of ovalness = distance from best-fitting ellipsoid

OVALNESS:

OVALNESS oval object best-fitting ellipse

PROTOFORM EQUIVALENCE:

PROTOFORM EQUIVALENCE A key concept in protoform theory is that of protoform-equivalence
At specified levels of abstraction, summarization and detail, p and q are protoform-equivalent, written in PFE(p, q), if p and q have identical protoforms at those levels
Example
p: most Swedes are tall
q: few professors are rich
Protoform equivalence serves as a basis
for protoform-centered mode of knowledge organization

PF-EQUIVALENCE:

PF-EQUIVALENCE Scenario A:
Alan has severe back pain. He goes to see a doctor. The doctor tells him that there are two options: (1) do nothing; and (2) do surgery. In the case of surgery, there are two possibilities: (a) surgery is successful, in which case Alan will be pain free; and (b) surgery is not successful, in which case Alan will be paralyzed from the neck down. Question: Should Alan elect surgery?

PF-EQUIVALENCE:

PF-EQUIVALENCE Scenario B:
Alan needs to fly from San Francisco to St. Louis and has to get there as soon as possible. One option is fly to St. Louis via Chicago and the other through Denver. The flight via Denver is scheduled to arrive in St. Louis at time a. The flight via Chicago is scheduled to arrive in St. Louis at time b, with a<b. However, the connection time in Denver is short. If the flight is missed, then the time of arrival in St. Louis will be c, with c>b. Question: Which option is best?

THE TRIP-PLANNING PROBLEM:

THE TRIP-PLANNING PROBLEM I have to fly from A to D, and would like to get there as soon as possible
I have two choices: (a) fly to D with a connection in B; or
(b) fly to D with a connection in C
if I choose (a), I will arrive in D at time t1
if I choose (b), I will arrive in D at time t2
t1 is earlier than t2
therefore, I should choose (a) ? A C B D (a) (b)

PROTOFORM EQUIVALENCE:

PROTOFORM EQUIVALENCE options gain c 1 2 a b 0

PROTOFORM-CENTERED KNOWLEDGE ORGANIZATION:

EXAMPLE:

EXAMPLE module submodule set of cars and their prices

Slide60:

PROTOFORMS AND LOGICAL FORMS p=proposition in a natural language
if p has a logical form, LF(p), then a protoform of p, PF(p), is an abstraction of LF(p)
all men are mortal x(man(x) mortal(x)) x(A(x) B(x)) p LF(p) PF(p)

Slide61:

CONTINUED if p does not have a logical form but is in PNL, then a protoform of p is an abstraction of the generalized constraint form of p, GC(p) most Swedes are tall ΣCount(tall.Swedes/Swedes) is most p GC(p) QA’s are B’s or Count(A/B) is Q PF(p) abstraction

Slide69:

PROTOFORMAL CONSTRAINT PROPAGATION Dana is young Age (Dana) is young X is A p GC(p) PF(p) Tandy is a few years older than Dana Age (Tandy) is (Age (Dana)) Y is (X+B) X is A
Y is (X+B)
Y is A+B Age (Tandy) is (young+few) +few

Slide70:

EXAMPLE OF DEDUCTION most Swedes are tall
? R Swedes are very tall most Swedes are tall Q A’s are B’s s/a-transformation Q A’s are B’s
Q½ A’s are 2B’s most½ Swedes are very tall r 1 1 0 0.25 0.5 most most

PROTOFORMAL DEDUCTIONTHE ROBERT EXAMPLE:

PROTOFORMAL DEDUCTION THE ROBERT EXAMPLE The Robert example is intended to serve as an illustration of protoformal deduction. In addition, it is intended to serve as a test of ability of standard probability theory, PT, to operate on perception-based information
IDS: Usually Robert returns from work at about 6 pm
TDS: What is the probability that Robert is home at about t pm?

SOLUTION:

SOLUTION Precisiation
p: usually Robert returns from work at about 6 pm
pp*: Prob(Return.Robert.from.work is about.6 pm
is usually)
What is the probability that Robert is home at about t pm?
qq*: Prob(Robert.home.at.about.t pm) is ? D
Abstraction
p*p**: Prob(X is A) is B
q*q**: Prob(Y is C) is ?D Y C D X A B

CONTINUED:

CONTINUED Search in Deduction Database
desired rule: Prob(X is A) is B
Prob(Y is C) is ?D
top-level agent reports that desired rule is not in DDB, but that a variant rule,
Prob(X is A) is B
Prob(X is C) is ?D ,
is in DDB
Can the desired rule be linked to the variant rule?

CONTINUED:

CONTINUED Computation
Prob(X is A) is B
Prob(X is C) is ?D
computational part (g: probability density of X) subject to

CONTINUED:

CONTINUED Search for linkage
If Robert does not leave his home after returning from work, then
Robert is at home at about.t pm =
Robert returns from work at.or.before t pm
consequently
Y is about t pm= X is  about.t pm

Slide76:

THE ROBERT EXAMPLE event equivalence Robert is home at about t pm= Robert returns from work
before about t pm 1 0 T t time time of return before t* t* (about t pm) Before about t pm= ≤ o about t pm 

CONTINUED:

CONTINUED Answer
Instantiation: D= Prob {Robert is home at about t}
X= Time (Robert returns from work)
A= 6*
B= usually
C=  t* subject to

CONCLUSION:

CONCLUSION addition of significant question-answering capability to search engines is a complex, open-ended problem
incremental progress, but not much more, is achievable through the use of bivalent-logic-based methods
to achieve significant progress, it is imperative to develop and employ techniques based on computing with words, protoform theory, precisiated natural language and computational theory of perceptions